The struggling and twitching of an electrically shocked body in low-voltage distribution grids can complicate and diversify the electric shock current, posing challenges to traditional electric shock detection methods. To tackle this issue, we propose a fault identification method for animal electric shocks considering unstable contact situations. First, a low-voltage distribution network grounding fault experimental platform is established in a real system, creating a sample library that encompasses various operational scenarios. Second, based on the dynamic characteristics of contact resistance and the principles of arc breakdown generation, an electrical equivalent modeling analysis is conducted on the unstable contact region. The underlying mechanisms that increase the complexity and diversity of electric shock current are revealed. Then, innovative features such as “the sum of periodic point differences,” “energy fluctuation arrays,” “pulse regularity,” and “the count of reversals in the direction of changes” are proposed. By integrating several traditional features and automatically extracted encoded features, a feature vector for animal electric shock faults is established from the perspectives of dynamic amplitude, high-frequency components, energy, and encoded features. In addition, a light gradient boosting machine optimized by multiattraction mechanism zebra optimization has been developed. This model achieves accuracy in pattern recognition tasks through its unique ensemble computation and one-sided sampling mechanism, while also reducing time and space costs. Finally, the proposed method is compared with three existing electric shock identification methods using our experimental platform's testing set. The results indicate that the proposed method demonstrates superiority in terms of accuracy for identifying animal electric shocks.

In self-supervised speaker verification, the quality of generated pseudo labels becomes a bottleneck for the performance. This work introduces a dynamic threshold within the iterative DIstillation with NO labels (DINO) framework. We employ a Gaussian Mixture Model (GMM) to model the loss distribution of the training data. The GMM has two components: one represents samples with reliable labels, and the other with un-reliable ones. These components help us determine a thresh-old for retaining samples with reliable labels. Furthermore, to take advantage of the different sensitivity of network layers to label noise, we further introduce hierarchical training to reduce the negative impact of unreliable labels. Compared to the baseline with a fixed threshold, our two strategies result in an 8.9% relative improvement on the Vox-O trial of the Voxceleb1 evaluation dataset.

The performance of downlink massive multiple-input- multiple-output networks with co-channel device-to- device communications is investigated in this paper. Specifically, we consider a cellular network with sufficient number of antennas at the base station and typical cell coverage, where the cell users and device-to-device transmitters are randomly distributed. In the analysis, the asymptotic signal- to-interference ratios for both downlink and device- to-device links are first obtained, which depend on the pathloss and small-scale fading of the interference channels. Since these information may not be available at the service base station or device-to-device transmitter, there exists a chance of packet outage. Therefore, we continue to derive the closed-form approximation of the average goodput, which measures the average number of information bits successfully delivered to the receiver. As a result, the system design trade-off between downlink and co-channel device-to-device communications can be investigated analytically. Moreover, the performance region in which the co- channel device-to-device communications could lead to better overall spectral efficiency can be obtained. Finally, it is shown by simulations that the analytical results matches the actual performance very well.

It has been a big challenge to develop a routing protocol that can meet different application needs and optimize routing paths according to the topology changes in mobile ad hoc networks. Basing their forwarding decisions only on local topology, geographic routing protocols have drawn a lot of attentions in recent years. However, there is a lack of holistic design for geographic routing to be more efficient and robust in a dynamic environment. Inaccurate local and destination position information can lead to inefficient geographic forwarding and even routing failure. The use of proactive fixed-interval beaconing to distribute local positions introduces high overhead when there is no traffic and cannot capture the topology changes under high mobility. It is also difficult to preset protocol parameters correctly to fit in different environments. In this work, we propose two self-adaptive on-demand geographic routing schemes which build efficient paths based on the need of user applications and adapt to various scenarios to provide efficient and reliable routing. To alleviate the impact due to inaccurate local topology knowledge, the topology information is updated at a node in a timely manner according to network dynamics and traffic demand. On-demand routing mechanism in both protocols reduces control overhead compared to the proactive schemes which are normally adopted in current geographic routing protocols. Additionally, our route optimization scheme adapts the routing path according to both topology changes and actual data traffic requirements. Furthermore, adaptive parameter setting scheme is introduced to allow each node to determine and adjust the protocol parameter values independently according to different network environments, data traffic conditions, and node's own conditions. Our simulation studies demonstrate that the proposed routing protocols are more robust and outperform the existing geographic routing protocol and conventional on-demand routing protocols under various conditions including different mobilities, node densities, traffic loads, and destination position inaccuracies. Specifically, the proposed protocols could reduce the packet delivery latency up to 80 percent as compared to GPSR at high mobility. Both routing protocols could achieve about 98 percent delivery ratios, avoid incurring unnecessary control overhead, have very low forwarding overhead and transmission delay in all test scenarios.

It has been a big challenge to develop routing protocol that can meet different application needs and optimize routing paths according to the topology change in mobile ad hoc networks. Basing their forwarding decisions only on the local topology, geographic routing protocols have drawn a lot of attentions in recent years. However, inaccurate local topology knowledge and the outdated destination position information can lead to inefficient geographic forwarding and even routing failure. Proactive local position distribution can hardly adapt to the traffic demand. It is also difficult to pre-set protocol parameters correctly to fit in different environments. We have developed two self-adaptive on-demand geographic routing schemes. The local topology is updated in a timely manner according to network dynamics and traffic demands. Our route optimization scheme adapts the routing path according to both topology changes and actual data traffic requirements. Each node can determine and adjust the protocol parameter values independently according to different network environments, data traffic conditions and node's own requirements. Our simulation studies have shown that the proposed routing protocols are more robust and outperform the existing geographic routing protocol. Specifically, the packet delivery latency is reduced almost four times as compared to GPSR at high mobility.

Group communications are important in mobile ad hoc networks (MANET). Multicast is an efficient method to implement the group communications. However, it is challenging to implement scalable, robust and efficient multicast in MANET due to the difficulty in group membership management, multicast packet forwarding and the maintenance of a tree-or mesh-based multicast structure over the dynamic topology for a large group size or network size. We propose a novel robust and scalable geographic multicast protocol (RSGM). Scalable and efficient group membership management has been performed through zone-based structure, and the location service for group members is combined with membership management. Both the control messages and data packets are forwarded along efficient tree-shape paths, but there is no need to actively maintain a tree structure, which efficiently reduces the maintenance overhead and makes the transmissions more robust to dynamics. Geographic forwarding is used to achieve further scalability and robustness. To avoid periodic flooding-based sources' announcements, an efficient source tracking mechanism is designed. Furthermore, we handle the empty zone problem faced by most zone-based routing protocols. Our simulation studies show that RSGM can scale to large group size and large network size, and a high delivery ratio is achieved by RSGM even under high dynamics.

Group communications is important in supporting multimedia applications. Multicast is an efficient method in implementing the group communications. However, it is challenging to implement efficient and scalable multicast in mobile ad hoc networks (MANET) due to the difficulty in group membership management and multicast packet forwarding over the dynamic topology. We propose a novel efficient geographic multicast protocol (EGMP). EGMP uses a zone-based structure to implement scalable and efficient group membership management and a network-range zone-based bi-directional tree is constructed to achieve a more efficient multicast delivery. The position information is used to guide the zone structure building, multicast tree construction and multicast packet forwarding, which efficiently reduces the overhead for route searching and tree structure maintenance. EGMP does not depend on any specific geographic unicast routing protocol. Several methods are assumed to further make the protocol efficient, for example, introducing the concept of zone depth for building an optimal tree structure and combining the location search of group members with the hierarchical group membership management. Finally, we design a scheme to handle empty zone problem faced by most routing protocols using a zone structure.

A typical data-dissemination sensor network consists of a large number of sensor nodes, which are normally energy-constrained and unlikely to be recharged in field. Redundant sensor nodes are often deployed to increase network robustness, and to extend network lifetime. Target and inquirer mobilities further bring more challenges to large-scale sensor networks. Frequent location updates for multiple inquirers and targets can drain the limited onboard energy excessively. We present EEDD, an energy-efficient data-dissemination protocol to address both the target and inquirer mobility problem and the energy conservation problem. We extend network lifetime by adopting a virtual-grid-based two-level architecture to schedule the activities of sensor nodes. Furthermore, we propose an adaptive scheduling scheme and a data dissemination scheme to reduce sensing delay and packet forwarding delay. The simulation results show that our protocol saves up to twelve times energy without much increased delay when compared with protocol not considering energy efficiency.